Anatomical landmarks are recognizable points within the body's structure. In medical imaging, anatomical landmarks may be used as reference points, for example to align or register related images. FIG. 1 illustrates a number of anatomical landmarks within the human body by way of example.
Accuracy of anatomical landmark location is important. For example, to use vascular landmarks as seeds for vessel tracking, accuracy of 5 mm or better may be required. It is necessary for a method of landmark location to be capable of dealing with the variations in anatomy resulting from different patients and different views.
There are various known methods for identifying landmarks. For example, identification of landmarks may be carried out manually, by an operator, but this is a lengthy process and results may vary between operators.
Alternatively, identification of landmarks may be performed automatically using classifiers. A classifier usually comprises an algorithm that allocates data items (in this context, points within an image) to categories (classes). A two-class classifier decides whether a point or points should be allocated to a first class or a second class. A multi-class classifier decides to which of a greater number of classes a point or points should be assigned.
A classifier is usually trained on multiple image data sets for each of which the location of the anatomical landmarks of interest is known. A probabilistic classifier outputs a likelihood or probability of a given point being in a particular class.
It is known to use multi-class classifiers to identify a plurality of anatomical landmarks within an image data set during a single procedure. Such multi-class classifiers are able to assign probabilities, for each point and for each landmark, that the point is within a region that contains the landmark (also referred to as a foreground region for that landmark, or a landmark region) or that the point is within a region that does not contain the landmark (also referred to as a background region). By then comparing the resulting probabilities for each point, the most likely position of each landmark can be selected.
However, the use of such multi-class classifiers can be inaccurate. Training the multiclass classifier usually requires selection of foreground and background regions for each landmark within the training data sets. As the background regions are usually much larger than the foreground regions, and as the number of background points and foreground points selected for training purposes may be similar, it can be the case that a relatively small number of background training points may have to be used to represent a large, diverse background region, leading to inaccuracy.
FIG. 2(a) illustrates foreground points 50 and background points 52 that may be used to train a single-stage (rather than nested) classifier. Points on or near the landmark (foreground points 50) are depicted with circles, and points that are not near the landmark (background points 52) are depicted with triangles. The classifier must be trained to distinguish foreground points from background points. If the classifier is probabilistic, it must be trained to output a probability or likelihood that a given point is foreground or background.
One approach to location of multiple landmarks is to train one multi-class classifier. For example, if three landmarks were to be located, a four-class classifier may be trained, with three landmark classes and one background class. Each landmark class would comprise points in the foreground of a respective landmark. This scenario is represented in FIG. 2(b), which shows background points 52 (triangles), first landmark foreground points 50 (circles), second landmark foreground points 54 (squares) and third landmark foreground points 56 (circles).
However, attempting to learn multiple landmarks simultaneously may require a complex decision surface. In order to achieve better accuracy, the foreground points for each landmark may be restricted to a small region around the landmark, resulting in a small number of training points. This may require that the number of background training points is similarly limited, if a classifier training method should require approximately equal numbers of samples from each of the classes. The small number of background training points may have to represent a huge variation in background voxels.